Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Cancer Cell International
Published in: Cancer Cell International 1/2024

Open Access 01-12-2024 | Colorectal Cancer | Research

Sparassis latifolia and exercise training as complementary medicine mitigated the 5-fluorouracil potent side effects in mice with colorectal cancer: bioinformatics approaches, novel monitoring pathological metrics, screening signatures, and innovative management tactic

Authors: Navid Abedpoor, Farzaneh Taghian, Khosro Jalali Dehkordi, Kamran Safavi

Published in: Cancer Cell International | Issue 1/2024

Login to get access

Abstract

Background

Prompt identification and assessment of the disease are essential for reducing the death rate associated with colorectal cancer (COL). Identifying specific causal or sensitive components, such as coding RNA (cRNA) and non-coding RNAs (ncRNAs), may greatly aid in the early detection of colorectal cancer.

Methods

For this purpose, we gave natural chemicals obtained from Sparassis latifolia (SLPs) either alone or in conjunction with chemotherapy (5-Fluorouracil to a mouse colorectal tumor model induced by AOM-DSS. The transcription profile of non-coding RNAs (ncRNAs) and their target hub genes was evaluated using qPCR Real-Time, and ELISA techniques.

Results

MSX2, MMP7, ITIH4, and COL1A2 were identified as factors in inflammation and oxidative stress, leading to the development of COL. The hub genes listed, upstream regulatory factors such as lncRNA PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p have been discovered as biomarkers for prognosis and diagnosis of COL. The SLPs and exercise, effectively decreased the size and quantity of tumors.

Conclusions

This effect may be attributed to the modulation of gene expression levels, including MSX2, MMP7, ITIH4, COL1A2, PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p. Ultimately, SLPs and exercise have the capacity to be regarded as complementing and enhancing chemotherapy treatments, owing to their efficacious components.
Literature
1.
2.
Rabeneck L, Chiu H-M, Senore C. International perspective on the burden of colorectal cancer and public health effects. Gastroenterology. 2020;158(2):447–52.PubMedCrossRef
3.
Shahbaz SK, Koushki K, Ayati SH, Bland AR, Bezsonov EE, Sahebkar A. Inflammasomes and colorectal cancer. Cells. 2021;10:9.
4.
Hajibabaie F, Abedpoor N, Assareh N, Tabatabaiefar MA, Shariati L, Zarrabi A. The importance of SNPs at miRNA binding sites as biomarkers of gastric and colorectal cancers: a systematic review. J Person Med. 2022;12(3):456.CrossRef
5.
Li J, Chen D, Shen M. Tumor microenvironment shapes colorectal cancer progression, metastasis, and treatment responses. Front Med. 2022;9: 869010.CrossRef
6.
Kaviani E, Hajibabaie F, Abedpoor N, Safavi K, Ahmadi Z, Karimy A. System biology analysis to develop diagnostic biomarkers, monitoring pathological indexes, and novel therapeutic approaches for immune targeting based on maggot bioactive compounds and polyphenolic cocktails in mice with gastric cancer. Environ Res. 2023;238: 117168.PubMedCrossRef
7.
Keller D, Windsor A, Cohen R, Chand M. Colorectal cancer in inflammatory bowel disease: review of the evidence. Tech Coloproctol. 2019;23:3–13.PubMedCrossRef
8.
Duarte D, Vale N. Combining repurposed drugs to treat colorectal cancer. Drug Disc Today. 2022;27(1):165–84.CrossRef
9.
Kelland L. The resurgence of platinum-based cancer chemotherapy. Nat Rev Cancer. 2007;7(8):573–84.PubMedCrossRef
10.
Huang X, Ke K, Jin W, Zhu Q, Zhu Q, Mei R, et al. Identification of genes related to 5-fluorouracil based chemotherapy for colorectal cancer. Front Immunol. 2022;13: 887048.PubMedPubMedCentralCrossRef
11.
Wilkinson NW, Yothers G, Lopa S, Costantino JP, Petrelli NJ, Wolmark N. Long-term survival results of surgery alone versus surgery plus 5-fluorouracil and leucovorin for stage II and stage III colon cancer: pooled analysis of NSABP C-01 through C-05. A baseline from which to compare modern adjuvant trials. Ann Surg Oncol. 2010;17:959–66.PubMedPubMedCentralCrossRef
12.
Sargent DJ, Patiyil S, Yothers G, Haller DG, Gray R, Benedetti J, et al. End points for colon cancer adjuvant trials: observations and recommendations based on individual patient data from 20,898 patients enrolled onto 18 randomized trials from the ACCENT Group. J Clin Oncol. 2007;25(29):4569–74.PubMedCrossRef
13.
Manfredi S, Bouvier A, Lepage C, Hatem C, Dancourt V, Faivre J. Incidence and patterns of recurrence after resection for cure of colonic cancer in a well defined population. J Br Surg. 2006;93(9):1115–22.CrossRef
14.
Miller KD, Nogueira L, Mariotto AB, Rowland JH, Yabroff KR, Alfano CM, et al. Cancer treatment and survivorship statistics, 2019. CA. 2019;69(5):363–85.PubMed
15.
Douillard J, Cunningham D, Roth A, Navarro M, James R, Karasek P, et al. Irinotecan combined with fluorouracil compared with fluorouracil alone as first-line treatment for metastatic colorectal cancer: a multicentre randomised trial. Lancet. 2000;355(9209):1041–7.PubMedCrossRef
16.
Giacchetti S, Perpoint B, Zidani R, Le Bail N, Faggiuolo R, Focan C, et al. Phase III multicenter randomized trial of oxaliplatin added to chronomodulated fluorouracil–leucovorin as first-line treatment of metastatic colorectal cancer. J Clin Oncol. 2000;18(1):136.PubMedCrossRef
17.
Xie P, Mo J-L, Liu J-H, Li X, Tan L-M, Zhang W, et al. Pharmacogenomics of 5-fluorouracil in colorectal cancer: review and update. Cell Oncol. 2020;43:989–1001.CrossRef
18.
Ke MJ, Ji LD, Li YX. Explore prognostic marker of colorectal cancer based on ceRNA network. J Cell Biochem. 2019;120(12):19358–70.PubMedCrossRef
19.
Xiang J, Gao L, Jing HY, Liu YX, Wang HF, Chang ZW, et al. Construction of CeRNA regulatory network based on WGCNA reveals diagnosis biomarkers for colorectal cancer. BMC Cancer. 2022;22(1):1–16.CrossRef
20.
21.
Seo CH, Kim J-R, Kim M-S, Cho K-H. Hub genes with positive feedbacks function as master switches in developmental gene regulatory networks. Bioinformatics. 2009;25(15):1898–904.PubMedCrossRef
22.
Geng F, Wang Z, Yin H, Yu J, Cao B. Molecular targeted drugs and treatment of colorectal cancer: recent progress and future perspectives. Cancer Biother Radiopharm. 2017;32(5):149–60.PubMed
23.
Tewari D, Patni P, Bishayee A, Sah AN, Bishayee A, editors. Natural products targeting the PI3K-Akt-mTOR signaling pathway in cancer: a novel therapeutic strategy. Seminars in cancer biology; 2022: Elsevier.
24.
Hojman P, Gehl J, Christensen JF, Pedersen BK. Molecular mechanisms linking exercise to cancer prevention and treatment. Cell Metab. 2018;27(1):10–21.PubMedCrossRef
25.
Berntsen S, Aaronson NK, Buffart L, Börjeson S, Demmelmaier I, Hellbom M, et al. Design of a randomized controlled trial of physical training and cancer (Phys-Can)–the impact of exercise intensity on cancer related fatigue, quality of life and disease outcome. BMC Cancer. 2017;17:1–12.CrossRef
26.
Gerritsen JK, Vincent AJ. Exercise improves quality of life in patients with cancer: a systematic review and meta-analysis of randomised controlled trials. Br J Sports Med. 2016;50(13):796–803.PubMedCrossRef
27.
Rock CL, Thomson C, Gansler T, Gapstur SM, McCullough ML, Patel AV, et al. American Cancer Society guideline for diet and physical activity for cancer prevention. CA. 2020;70(4):245–71.PubMed
28.
Duan G-L, Yu X-B. Isolation, purification, characterization, and antioxidant activity of low-molecular-weight polysaccharides from Sparassis latifolia. Int J Biol Macromol. 2019;137:1112–20.PubMedCrossRef
29.
Liu J, Zhang X, Zhang J, Yan M, Li D, Zhou S, et al. Research on extraction, structure characterization and immunostimulatory activity of cell wall polysaccharides from Sparassis latifolia. Polymers. 2022;14(3):549.PubMedPubMedCentralCrossRef
30.
Wei X, Cheng F, Liu J, Cheng Y, Yun S, Meng J, et al. Sparassis latifolia polysaccharides inhibit colon cancer in mice by modulating gut microbiota and metabolism. Int J Biol Macromol. 2023;232: 123299.PubMedCrossRef
31.
Jayaraman P, Rodrik-Outmezguine V, Millholland J, O’Brien N, Wong C, Diwanji R, et al. Targeting tumor-promoting inflammation (TPI) via the IL-1βpathway for cancer immunotherapy. Cancer Res. 2020;80(16):5640.CrossRef
32.
Peng L, Bian XW, Li DK, Xu C, Wang GM, Xia QY, et al. Large-scale RNA-Seq transcriptome analysis of 4043 cancers and 548 normal tissue controls across 12 TCGA cancer types. Sci Rep. 2015;5(1):13413.PubMedPubMedCentralCrossRef
33.
34.
Mocellin S. Microarray technology and cancer gene profiling: Springer Science & Business Media; 2007.
35.
Eilertsen IA, Moosavi SH, Strømme JM, Nesbakken A, Johannessen B, Lothe RA, et al. Technical differences between sequencing and microarray platforms impact transcriptomic subtyping of colorectal cancer. Cancer Lett. 2020;469:246–55.PubMedCrossRef
36.
Canales RD, Luo Y, Willey JC, Austermiller B, Barbacioru CC, Boysen C, et al. Evaluation of DNA microarray results with quantitative gene expression platforms. Nat Biotechnol. 2006;24(9):1115–22.PubMedCrossRef
37.
Alameer A, Chicco D. geoCancerPrognosticDatasetsRetriever: a bioinformatics tool to easily identify cancer prognostic datasets on Gene Expression Omnibus (GEO). Bioinformatics. 2022;38(6):1761–3.PubMedCrossRef
38.
Liu DD, Zhang L. Trends in the characteristics of human functional genomic data on the gene expression omnibus, 2001–2017. Lab Invest. 2019;99(1):118–27.PubMedCrossRef
39.
Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 2016;44(8):e71.PubMedCrossRef
40.
Pereira MB, Wallroth M, Jonsson V, Kristiansson E. Comparison of normalization methods for the analysis of metagenomic gene abundance data. BMC Genomics. 2018;19:1–17.CrossRef
41.
Chen Y, McCarthy D, Robinson M, Smyth GK. edgeR: differential expression analysis of digital gene expression data User's Guide. Bioconductor User's Guide. 2014.
42.
Vlachavas EI, Pilalis E, Papadodima O, Koczan D, Willis S, Klippel S, et al. Radiogenomic analysis of F-18-fluorodeoxyglucose positron emission tomography and gene expression data elucidates the epidemiological complexity of colorectal cancer landscape. Comput Struct Biotechnol J. 2019;17:177–85.PubMedPubMedCentralCrossRef
43.
Coussement L, Van Criekinge W, De Meyer T. Quantitative transcriptomic and epigenomic data analysis: a primer. Bioinform Adv. 2024;1:vbae019.CrossRef
44.
Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023;51(D1):D638–46.PubMedCrossRef
45.
Otasek D, Morris JH, Bouças J, Pico AR, Demchak B. Cytoscape automation: empowering workflow-based network analysis. Genome Biol. 2019;20:1–15.CrossRef
46.
Majeed A, Mukhtar S. Protein–Protein Interaction Network Exploration Using Cytoscape. Protein-Protein Interactions: Methods and Protocols: Springer; 2023. p. 419–27.
47.
Bastian M, Heymann S, Jacomy M, editors. Gephi: an open source software for exploring and manipulating networks. Proceedings of the international AAAI conference on web and social media; 2009.
48.
Majeed S, Uzair M, Qamar U, Farooq A, editors. Social Network Analysis Visualization Tools: A Comparative Review. 2020 IEEE 23rd International Multitopic Conference (INMIC); 2020: IEEE.
49.
Evangelista JE, Xie Z, Marino GB, Nguyen N, Clarke DJ, Ma’ayan A. Enrichr-KG: bridging enrichment analysis across multiple libraries. Nucleic Acids Res. 2023;51(W1):W168–79.PubMedPubMedCentralCrossRef
50.
51.
Doncheva NT, Morris JH, Gorodkin J, Jensen LJ. Cytoscape StringApp: network analysis and visualization of proteomics data. J Proteome Res. 2018;18(2):623–32.PubMedPubMedCentralCrossRef
52.
Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45(W1):W98–102.PubMedPubMedCentralCrossRef
53.
Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, et al. TIMER2. 0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48(W1):W509-W14.
54.
Chen G, Wang Z, Wang D, Qiu C, Liu M, Chen X, et al. LncRNADisease: a database for long-non-coding RNA-associated diseases. Nucleic Acids Res. 2012;41(D1):D983–6.PubMedPubMedCentralCrossRef
55.
Zhao H, Yin X, Xu H, Liu K, Liu W, Wang L, et al. LncTarD 20: an updated comprehensive database for experimentally-supported functional lncRNA–target regulations in human diseases. Nucleic Acids Res. 2023;51(D1):D199–207.PubMedCrossRef
56.
Marino GB, Wojciechowicz ML, Clarke DJ, Kuleshov MV, Xie Z, Jeon M, et al. lncHUB2: aggregated and inferred knowledge about human and mouse lncRNAs. Database. 2023;2023(1):baad009.PubMedPubMedCentralCrossRef
57.
Ning L, Cui T, Zheng B, Wang N, Luo J, Yang B, et al. MNDR v3. 0: mammal ncRNA–disease repository with increased coverage and annotation. Nucleic Acids Res. 2021;49(D1):D160-D4.
58.
Chen J, Zhang J, Gao Y, Li Y, Feng C, Song C, et al. LncSEA: a platform for long non-coding RNA related sets and enrichment analysis. Nucleic Acids Res. 2021;49(D1):D969–80.PubMedCrossRef
59.
Kang J, Tang Q, He J, Li L, Yang N, Yu S, et al. RNAInter v4. 0: RNA interactome repository with redefined confidence scoring system and improved accessibility. Nucleic acids research. 2022;50(D1):D326-D32.
60.
Li Z, Liu L, Feng C, Qin Y, Xiao J, Zhang Z, et al. LncBook 20: integrating human long non-coding RNAs with multi-omics annotations. Nucleic Acids Res. 2023;51(D1):D186–91.PubMedCrossRef
61.
Chang L, Xia J. MicroRNA regulatory network analysis using miRNet 2.0. Transcription Factor Regulatory Networks: Springer; 2022. p. 185–204.
62.
Chang L, Zhou G, Soufan O, Xia J. miRNet 20: network-based visual analytics for miRNA functional analysis and systems biology. Nucleic Acids Res. 2020;48(W1):W244–51.PubMedPubMedCentralCrossRef
63.
D’Arrigo G, Leonardis D, Abd ElHafeez S, Fusaro M, Tripepi G, Roumeliotis S. Methods to analyse time-to-event data: the Kaplan-Meier survival curve. Oxid Medi Cell Long. 2021;2021.
64.
Freshour SL, Kiwala S, Cotto KC, Coffman AC, McMichael JF, Song JJ, et al. Integration of the Drug–Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts. Nucleic acids research. 2021;49(D1):D1144-D51.
65.
Gilson MK, Liu T, Baitaluk M, Nicola G, Hwang L, Chong J. BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res. 2016;44(D1):D1045–53.PubMedCrossRef
66.
Burley SK, Bhikadiya C, Bi C, Bittrich S, Chao H, Chen L, et al. RCSB Protein Data Bank (RCSB. org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning. Nucleic Acids Res. 2023;51(D1):D488-D508.
67.
Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, et al. PubChem 2023 update. Nucleic Acids Res. 2023;51(D1):D1373–80.PubMedCrossRef
68.
O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open babel: an open chemical toolbox. J Cheminform. 2011;3(1):1–14.CrossRef
69.
Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, et al. UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem. 2004;25(13):1605–12.PubMedCrossRef
70.
Dallakyan S, Olson AJ. Small-molecule library screening by docking with PyRx. Chemical biology: methods and protocols. 2015:243–50.
71.
Yuan S, Chan HS, Hu Z. Using PyMOL as a platform for computational drug design. Wiley Interdisciplinary Reviews: Computational Molecular Science. 2017;7(2): e1298.
72.
Bhachoo J, Beuming T. Investigating protein–peptide interactions using the Schrödinger computational suite. Modeling peptide-protein interactions. 2017:235–54.
73.
Jeon H-J, Yeom Y, Kim Y-S, Kim E, Shin J-H, Seok PR, et al. Effect of vitamin C on azoxymethane (AOM)/dextran sulfate sodium (DSS)-induced colitis-associated early colon cancer in mice. Nurs Res Pract. 2018;12(2):101–9.
74.
Kim MJ, Han Q, Bouvet M, Hoffman RM, Park JH. Recombinant oral methioninase (o-rMETase) combined with oxaliplatinum plus 5-fluorouracil improves survival of mice with massive colon-cancer peritoneal carcinomatosis. Anticancer Res. 2023;43(1):19–24.PubMedCrossRef
75.
Nitschke J, Modick H, Busch E, Von Rekowski RW, Altenbach H-J, Mölleken H. A new colorimetric method to quantify β-1, 3–1, 6-glucans in comparison with total β-1, 3-glucans in edible mushrooms. Food Chem. 2011;127(2):791–6.PubMedCrossRef
76.
Yang C, Song J, Hwang S, Choi J, Song G, Lim W. Apigenin enhances apoptosis induction by 5-fluorouracil through regulation of thymidylate synthase in colorectal cancer cells. Redox Biol. 2021;47: 102144.PubMedPubMedCentralCrossRef
77.
Blondy S, David V, Verdier M, Mathonnet M, Perraud A, Christou N. 5-Fluorouracil resistance mechanisms in colorectal cancer: From classical pathways to promising processes. Cancer Sci. 2020;111(9):3142–54.PubMedPubMedCentralCrossRef
78.
Kanduri J, More LA, Godishala A, Asnani A. Fluoropyrimidine-associated cardiotoxicity. Cardiol Clin. 2019;37(4):399–405.PubMedCrossRef
79.
Rtibi K, Marzouki L, Sebai H. Oxidative stress due to 5-fluorouracil and dietary antioxidants. Toxicology: Elsevier; 2021. p. 291–5.
80.
Abedpoor N, Taghian F, Hajibabaie F. Physical activity ameliorates the function of organs via adipose tissue in metabolic diseases. Acta Histochem. 2022;124(2): 151844.PubMedCrossRef
81.
Hajibabaie F, Abedpoor N, Taghian F, Safavi K. A cocktail of polyherbal bioactive compounds and regular mobility training as senolytic approaches in age-dependent alzheimer’s: the in silico analysis, lifestyle intervention in old age. J Mol Neurosci. 2023;73(2–3):171–84.PubMedCrossRef
82.
Wang T, Zhang Y, Taaffe DR, Kim J-S, Luo H, Yang L, et al. Protective effects of physical activity in colon cancer and underlying mechanisms: a review of epidemiological and biological evidence. Crit Rev Oncol Hematol. 2022;170: 103578.PubMedCrossRef
83.
Uchida M, Horii N, Hasegawa N, Oyanagi E, Yano H, Iemitsu M. Sparassis crispa intake improves the reduced lipopolysaccharide-induced TNF-α production that occurs upon exhaustive exercise in mice. Nutrients. 2019;11(9):2049.PubMedPubMedCentralCrossRef
84.
Nowacka-Jechalke N, Nowak R, Lemieszek MK, Rzeski W, Gawlik-Dziki U, Szpakowska N, et al. Promising potential of crude polysaccharides from Sparassis crispa against colon cancer: An in vitro study. Nutrients. 2021;13(1):161.PubMedPubMedCentralCrossRef
85.
Bennardi M, Diviani N, Gamondi C, Stüssi G, Saletti P, Cinesi I, et al. Palliative care utilization in oncology and hemato-oncology: a systematic review of cognitive barriers and facilitators from the perspective of healthcare professionals, adult patients, and their families. BMC Palliat Care. 2020;19:1–17.CrossRef
86.
Wu A, Colón GR, Lim M. Quality of life and role of palliative and supportive care for patients with brain metastases and caregivers: a review. Front Neurol. 2022;13: 806344.PubMedPubMedCentralCrossRef
87.
Mastrogamvraki N, Zaravinos A. Signatures of co-deregulated genes and their transcriptional regulators in colorectal cancer. NPJ Syst Biol Appl. 2020;6(1):23.PubMedPubMedCentralCrossRef
88.
Hajibabaie F, Kouhpayeh S, Mirian M, Rahimmanesh I, Boshtam M, Sadeghian L, et al. MicroRNAs as the actors in the atherosclerosis scenario. J Physiol Biochem. 2020;76:1–12.PubMedCrossRef
89.
Rafat M, Moraghebi M, Afsa M, Malekzadeh K. The outstanding role of miR-132-3p in carcinogenesis of solid tumors. Hum Cell. 2021;34(4):1051–65.PubMedCrossRef
90.
Wu HY, Xia S, Liu AG, Wei MD, Chen ZB, Li YX, et al. Upregulation of miR-132-3p in cholangiocarcinoma tissues: A study based on RT-qPCR, The Cancer Genome Atlas miRNA sequencing, Gene Expression Omnibus microarray data and bioinformatics analyses. Mol Med Rep. 2019;20(6):5002–20.PubMedPubMedCentral
91.
Huang J, Lu D, Xiang T, Wu X, Ge S, Wang Y, et al. MicroRNA-132-3p regulates cell proliferation, apoptosis, migration and invasion of liver cancer by targeting Sox4. Oncol Lett. 2020;19(4):3173–80.PubMedPubMedCentral
92.
Niespolo C, Johnston JM, Deshmukh SR, Satam S, Shologu Z, Villacanas O, et al. Tribbles-1 expression and its function to control inflammatory cytokines, including interleukin-8 levels are regulated by miRNAs in macrophages and prostate cancer cells. Front Immunol. 2020;11: 574046.PubMedPubMedCentralCrossRef
93.
Su Y, Shetty A, Jiang F. Integrated analysis of miRNAs and DNA methylation identifies miR-132-3p as a tumor suppressor in lung adenocarcinoma. Thoracic Cancer. 2020;11(8):2112–24.PubMedPubMedCentralCrossRef
94.
Li S, Xu JJ, Zhang QY. MicroRNA-132-3p inhibits tumor malignant progression by regulating lysosomal-associated protein transmembrane 4 beta in breast cancer. Cancer Sci. 2019;110(10):3098–109.PubMedPubMedCentralCrossRef
95.
Han S, Lin F, Ruan Y, Zhao S, Yuan R, Ning J, et al. miR-132-3p promotes the cisplatin-induced apoptosis and inflammatory response of renal tubular epithelial cells by targeting SIRT1 via the NF-κB pathway. Int Immunopharmacol. 2021;99: 108022.PubMedCrossRef
96.
Li C, Lu K, Lang C, Min L, Yang C. Research on the mechanism of miR-132–3p in inhibiting the proliferation, invasion and metastasis of colon cancer by targeted regulation of PDGF. 2023.
97.
Huang R, Xiang G, Duan X, Wang H, He K, Xiao J. MiR-132-3p inhibits proliferation, invasion and migration of colorectal cancer cells via down-regulating FOXP2 expression. Acta Biochim Pol. 2022;69(2):371–7.PubMed
98.
Zhang M, Li Y, Wang H, Yu W, Lin S, Guo J. LncRNA SNHG5 affects cell proliferation, metastasis and migration of colorectal cancer through regulating miR-132-3p/CREB5. Cancer Biol Ther. 2019;20(4):524–36.PubMedCrossRef
99.
Shaath H, Toor SM, Nada MA, Elkord E, Alajez NM. Integrated whole transcriptome and small RNA analysis revealed multiple regulatory networks in colorectal cancer. Sci Rep. 2021;11(1):14456.PubMedPubMedCentralCrossRef
Metadata
Title
Sparassis latifolia and exercise training as complementary medicine mitigated the 5-fluorouracil potent side effects in mice with colorectal cancer: bioinformatics approaches, novel monitoring pathological metrics, screening signatures, and innovative management tactic
Authors
Navid Abedpoor
Farzaneh Taghian
Khosro Jalali Dehkordi
Kamran Safavi
Publication date
01-12-2024
Publisher
BioMed Central
Published in
Cancer Cell International / Issue 1/2024
Electronic ISSN: 1475-2867
DOI
https://doi.org/10.1186/s12935-024-03328-y

Other articles of this Issue 1/2024

Cancer Cell International 1/2024 Go to the issue

Research

Enhancing therapeutic efficacy in luminal androgen receptor triple-negative breast cancer: exploring chidamide and enzalutamide as a promising combination strategy

Research

mir-605-3p prevents liver premetastatic niche formation by inhibiting angiogenesis via decreasing exosomal nos3 release in gastric cancer

Research

Toxoplasma gondii suppresses proliferation and migration of breast cancer cells by regulating their transcriptome

Research

Targeting HER2-positive breast cancer cells by a combination of dasatinib and BMS-202: Insight into the molecular pathways

Research

Forkhead box E1, frequently downregulted by promoter methylation, inhibits colorectal cancer cell growth and migration

Review

Melanoma biology and treatment: a review of novel regulated cell death-based approaches
Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras
Prof. Fabrice Barlesi
Developed by: Springer Medicine
Image Credits